ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 American International Journal of Research in Science, Technology, Engineering & Mathematics AIJRSTEM 13-155; © 2013, AIJRSTEM All Rights Reserved Page 165 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research) Available online at http://www.iasir.net Improved technique for object detection using Similarity Based Region Merging Garima Singh Rawat 1 , Joy Bhattacharjee 2 , Roopali Soni 3 1 M.Tech Student (IV Sem) 2,3 Asst. Prof. Dept. of C.S.E Oriental College of Technology, Bhopal (M.P.), INDIA I. Introduction Image Segmentation is a process which partitioned image into multiple unique regions, where region is set of similar pixels. If I is set of all image pixels, then by applying segmentation we get different-different unique regions like {R 1 ,R 2 ,R 3 ,..,R n } which when combined formed the image I. Many approaches have been proposed earlier which includes probabilistic graphical models[1], normalized cuts[2], graph-cut method [3] ,region growing[4] etc. .The automatic segmentation approaches may also fail even though they use prior information in segmentation process. The main reason behind this is the complexity of object segmentation of image in real applications. The complexity is due to the several reasons like shadow, low contrast areas, occlusion, cluttering and noise in the image. These reasons make the segmentation process quite difficult and challenging. The use of interactive image segmentation process is the solutions of such problems. In the interactive image segmentation the proper user’s intervention is required for segmentation process. Because user gives the clue for segmenting the image in the process the results are more improved and satisfactory .Therefore Semi-automatic or interactive segmentation method is proposed, which use human expert knowledge as additional input and makes the segmentation problem more tractable. The goal of semi- interactive segmentation methods is to minimize the required user interaction time, while maintaining tight user control to guarantee the correctness of the results .In image segmentation image similarity measure play an important role. Region merging for object retrieval is an important task in many image processing applications. It has a wide application in area of crime prevention, intellectual properties, medical diagnosis, web searching etc. The object segmentation results are very much influence with how to groups sub regions. So, accurate object segmentation is possible if we combine both high level and low level priors effectively. To achieve this we introduce in our paper new approach object segmentation with region labeling and region growing. II. Previous Work Lei Zhang, Zhi Zeng, and Qiang Ji [5] proposed a method to extend the Chain Graph (CG) model to with more general topology and the associated methods for learning and inference.CG is a hybrid Probabilistic Graphical Model (PGM) which Contains both directed and undirected links. Its representation is powerful enough to capture heterogeneous relationships among image entities. For CG they first oversegment the image into superpixels and find out different heterogeneous relationships among image entities (superpixels, vertices or junctions, edges, regions etc.) They construct the CG model with parameterization of links with derived Joint Probability Distribution (JPD). They represent these links by either potential function or conditional probabilities. They first create a Directed Master Graph then create directed sub-graphs for some terms in the Abstract: Object segmentation is an important task in image processing and computer vision. We address a method to effectively discover a user’s concept when object of interest are involved in input image. In this paper our method corporate frameworks for object retrieval using semi-automatic method for object detection because fully automatic segmentation is very hard for natural images. To improve the effectiveness of region merging we propose a new similarity based object retrieval. The users only need to roughly indicate the after which steps desired objects closed contour is obtained during the automatic merging of similar regions. A similarity based region merging mechanism is proposed to guide the merging process with the help of initial segmentation technique. Any two or more regions are merged with its adjacent regions on the basis of similarity. The proposed method automatically merges the regions that are initially segmented through initial segmentation technique, and then effectively extracts the object contour by merging regions. Keywords: Initial segmentation, similar regions, similarity distance measure, region merging, flood fill.